AI Model Identifies Target Pixel Positions in Physiological Images
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Solution Overview
Problem
Current methods for detecting Barrett's esophagus, such as gastroscopy, heavily rely on the experience and physical state of the doctor, leading to inefficiencies and potential inaccuracies in preliminary diagnoses and biopsy selections.
Innovation Solution
A medical auxiliary information generation method and system utilizing artificial intelligence models to analyze physiological images, identify target image regions, determine specific pixel positions, and generate medical auxiliary information that carries prompt information related to these positions, thereby improving detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual preliminary diagnosis and biopsy selection are performed by doctors, then diagnostic accuracy can be achieved through human expertise, but detection efficiency is reduced due to dependency on doctor's experience and physical state
Solution Approach 1:
An artificial intelligence model is introduced as an intermediary between the physiological image and the doctor's decision-making process. The AI model processes the image data and generates auxiliary information including target pixel positions and estimation values, serving as a mediator that enhances both diagnostic reliability and detection efficiency by automating the preliminary analysis while preserving human oversight
Solution Approach 2:
The manual mechanical process of visual inspection and subjective judgment by doctors is replaced with an automated computational system. The AI model substitutes the mechanical human cognitive process with algorithmic image analysis, automatically identifying target regions and generating quantitative estimation values, thereby improving both efficiency and consistency
2Adaptability or versatility
If manual biopsy position selection is performed by doctors, then flexibility in decision-making is maintained, but measurement precision is reduced due to subjectivity in region identification
Solution Approach 1:
The system transforms subjective visual assessment into objective quantitative parameters. The AI model generates estimation values and identifies specific target pixel positions with numerical precision, converting the qualitative doctor's judgment into measurable, reproducible data points that maintain flexibility while improving measurement precision
Solution Approach 2:
The AI model creates a digital copy or representation of the physiological image with annotated target regions and estimation values. This digital copy preserves all original image information while adding precise quantitative measurements and highlighted regions, allowing doctors to review the analysis without losing the ability to make flexible decisions based on the enhanced information
Data Source
AI summary
A medical auxiliary information generation method and a medical auxiliary information generation system are provided. The method includes: obtaining a physiological image; identifying a target image region from the physiological image through at least one artificial intelligence model, wherein each pixel position in the target image region corresponds to an estimation value, and the estimation value meets a default condition; determining at least one target pixel position in the target image region according to a numerical distribution of the estimation value; and generating medical auxiliary information according to the target pixel position, wherein the medical auxiliary information carries prompt information related to the target pixel position.